ArticleNature communications2022
Analysis of the first genetic engineering attribution challenge.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Resilience of recombinant antibiotic resistance gene-containing plasmids against common cell culture disposal methods.Cell reports methods · 2026Article
- PlasmidGPT: A generative framework for plasmid analysis and generation.Science advances · 2026Article
- Self-authenticating genomic materials in Escherichia coli via advanced genome signatures.Communications biology · 2025Article
- The need for adaptability in detection, characterization, and attribution of biosecurity threats.Nature communications · 2024Review
- Cryptographic approaches to authenticating synthetic DNA sequences.Trends in biotechnology · 2024Review
- Synsor: a tool for alignment-free detection of engineered DNA sequences.Frontiers in bioengineering and biotechnology · 2024Article
- Identifying widespread and recurrent variants of genetic parts to improve annotation of engineered DNA sequences.PloS one · 2024Article
- Analysis of the first genetic engineering attribution challenge.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to identify the designer of engineered biological sequences-termed genetic engineering attribution (GEA)-would help ensure due credit for biotechnological innovation, while holding designers accountable to the communities they affect. Here, we present the results of the first Genetic Engineering Attribution Challenge, a public data-science competition to advance GEA techniques. Top-scoring teams dramatically outperformed previous models at identifying the true lab-of-origin of engineered plasmid sequences, including an increase in top-1 and top-10 accuracy of 10 percentage points. A simple ensemble of prizewinning models further increased performance. New metrics, designed to assess a model's ability to confidently exclude candidate labs, also showed major improvements, especially for the ensemble. Most winning teams adopted CNN-based machine-learning approaches; however, one team achieved very high accuracy with an extremely fast neural-network-free approach. Future work, including future competitions, should further explore a wide diversity of approaches for bringing GEA technology into practical use.
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Registered trials
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